CN104502451B - Method for identifying flaw of steel plate - Google Patents
Method for identifying flaw of steel plate Download PDFInfo
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- CN104502451B CN104502451B CN201410778077.1A CN201410778077A CN104502451B CN 104502451 B CN104502451 B CN 104502451B CN 201410778077 A CN201410778077 A CN 201410778077A CN 104502451 B CN104502451 B CN 104502451B
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Abstract
The invention relates to a method for identifying a flaw of a steel plate. The method comprises the steps of processing an ultrasonic echo signal to obtain a wave curve, intercepting the wave curve to acquire a flaw wave curve, calculating the wave curve to extract peak data to obtain a flaw-type data group, further processing the flaw-type data group, and finally acquiring the flaw type of the steel plate by utilizing a flaw threshold value judging method and a morphology analysis method. According to the method for identifying the flaw of the steel plate, the flaw of the steel plate can be identified only according to the echo signal acquired by an ultrasonic probe device, the calculation speed is high, rapidness and accuracy in identification can be realized, a great amount of sample data is not needed for training, the data calculation amount is reduced, the identification speed is increased, and the flaw identification cost is reduced.
Description
Technical field
The present invention relates to a kind of method carrying out steel plate defect type automatic identification using ultrasound wave.
Background technology
With developing rapidly of China's industrial construction, the demand of steel plate can be increasing, the requirement to its inherent quality
More and more higher.Medium plate may form layering in process of production, crackle, diffusion-type are mingled with exceeded, white point, segregation and hydrogen
The various defect such as fracturing stricture of vagina.At present, domestic in terms of middle thickness high intensity steel intralaminar part quality judging, depend on ultrasound wave inspection
Survey technology.
Large-scale steel mill is many to be detected to steel plate defect using large-scale fixed inspection system, is primarily adapted for use in high-volume sizing
Steel plate detects.Additionally, being also applied to small lot, the testing equipment of small-sized steel plate defect detection.
Authorization Notice No. is the China of cn202693526u, cn202101975u, cn201141855y, cn201503418u
Utility model patent, and the Chinese invention of Application No. 201310750784.5 (application publication number is cn103698409a) is special
Profit application, wherein disclosed ultrasound wave steel sheet detector all detects to it that structure and monitoring principle have made detailed elaboration.On
State several steel sheet detectors and the position of defect and its equivalent size can be detected it is impossible to the type of identification defect.Again because not
The defect of same type requires difference to plate quality grading, so, the accurate judgement to defect type makes for the safety of steel plate
With having very important significance.It is presently mainly the shape of the ultrasonic Flaw a ripple signal being collected according to ultrasonic instrument, according to
The flaw detection experience of bad testing staff is manually judged, so can inevitably introduce error.
" a kind of extraction surpasses the Chinese invention patent of Patent No. zl97109099.8 (Authorization Notice No. is cn1065961c)
The method of the spectral amplitude phase information of sound echo-signal ", the frequency spectrum of ultrasonic Flaw signal is wherein obtained using Fourier transform.
Chinese invention patent " the number of ultrasonic signal of Patent No. zl200410011403.2 (Authorization Notice No. is cn100410925c)
Word signal processing method ", wherein joint time frequency analysis are carried out by lifting wavelet transform to ultrasonic Flaw signal, extract defect letter
Energy feature number in different frequency range.The above feature extracting method is all based on Hilbert transform and Fourier becomes
Change, extraction rate is slower.In terms of defect inspection, conventional method is pattern recognition, many of which grader.As special
For zl200710059575.0 (Authorization Notice No. is cn100567978c), " ultrasonic phased array detects Chinese invention patent profit number
Oil gas pipeline girth weld defect type automatic identifying method ", it is by Lifting Wavelet wherein using defect type automatic identifying method
Conversion is combined with fractal technology, and based on the automatic identifying method of supporting vector machine model, the method needs to be supported vector
The training of machine model, will obtain that the training sample number that higher recognition correct rate needs is larger, and the obtaining of a large amount of training sample
Take and be difficult to realize, it uses does not have popularity.
Content of the invention
The technical problem to be solved is to provide one kind to be capable of identify that steel plate defect class for above-mentioned prior art
Type and the steel plate defect recognition methodss that recognition speed is fast, recognition accuracy is high.
The present invention the adopted technical scheme that solves the above problems is: a kind of steel plate defect recognition methodss it is characterised in that:
Comprise the following steps:
Step one, Ultrasound Instrument start and initialize, and host computer sends control parameter to Ultrasound Instrument；
Step 2, ultrasonic probe device start and are operated according to the control parameter in Ultrasound Instrument, ultrasonic probe device
Tested steel plate moves, host computer obtains the real-time location coordinates data to steel plate test point for the ultrasonic probe device, form inspection
Coordinate data group the w=[(x of measuring point1,y1),(x2,y2),...,(xj,yj),...,(xb,yb)], wherein j and b is positive integer,
1≤j≤b, b are test point sum, (xj,yj) be j-th test point position coordinateses, xjRepresent that j-th test point is long in steel plate
Value on degree direction, yjRepresent j-th test point value on steel plate width direction；
Ultrasonic probe device launches ultrasound wave to tested steel plate, receives the ultrasonic echo signal from steel plate simultaneously, its
In, ultrasonic echo signal include from surface of steel plate reflection initial signal, from defective locations reflection flaw indication, from steel plate
The bottom ripple signal of bottom reflection；
Step 3, Ultrasound Instrument gather and store the echo-signal of each test point that ultrasonic probe device returns；
Step 4, Ultrasound Instrument are processed to echo-signal according to the velocity of sound in steel plate and gain, and then for each detection
Point position, depth-amplitude wavy curve that in the range of the tested steel plate of corresponding acquisition, echo-signal is formed, thus formed depth-
Amplitude wavy curve data set a=[a1,a2,...,aj,...,ab], wherein j and b is positive integer, and 1≤j≤b, b are detection
Point sum, ajRepresent the depth-amplitude wavy curve of j-th test point；
Ultrasound Instrument uploads to the curve data in depth-amplitude wavy curve data set a in host computer；
Step 5, host computer carry out subsequent treatment to depth-amplitude wavy curve data set a；
First, obtain each test point corresponding initial signal amplitude from depth-amplitude wavy curve data set a, from
And form initial signal amplitude data group i=[i1,i2,...,ij,...,ib], from depth-amplitude wavy curve data set a
Obtain each test point corresponding bottom wave amplitude, thus forming bottom wave amplitude data set d=[d1,d2,...,dj,...,db]；
Calculate initial signal amplitude meansigma methodss
Calculate bottom wave amplitude meansigma methodss
Wherein j and b is natural number, and 1≤j≤b, b are test point sum, djRepresent that j-th test point is corresponding at it
Depth-amplitude wavy curve ajUpper corresponding bottom ripple signal amplitude；
Pretreatment is carried out to depth-amplitude wavy curve data set a, that is, to every in depth-amplitude wavy curve data set a
Corresponding depth-amplitude the wavy curve of individual test point is intercepted, and retains in surface to the bottom surface depth bounds of tested steel plate and returns
The corresponding waveform of ripple signal, thus form defective waveform curve data group b=[b1,b2,...,bj,...,bb], wherein j and b is equal
For positive integer, 1≤j≤b, bjRepresent the defective waveform curve of j-th test point, b is test point sum；
Derived function is carried out to each test point corresponding defective waveform curve in defective waveform curve data group b, thus
Obtain all wave crest points in corresponding test point defective waveform curve, thus building wave crest point information data group c=[c1,
c2,...,cj,...,cb]；cj=[c[j][1],c[j][2]], c[j][1]=(sj0,sj1,...,sji,...,sja), c[j][2]=(fj0,
fj1,...,fji,...,fja)；
Wherein j and b is natural number, and 1≤j≤b, b are test point sum, cjRepresent the defective waveform of j-th test point
Curve bjIn all wave crest point information data groups of comprising；I and a is natural number, and 0≤i≤a, a are wave crest point sum, c[j][1]
Represent the depth of defect array of j-th test point, sjiDefective waveform curve b for j-th test pointjIn i-th wave crest point pair
The depth of defect value answered, c[j][2]For the defect amplitudes array of j-th test point, fjiDefective waveform curve for j-th test point
bjIn the corresponding flaw indication amplitude of i-th wave crest point；
Arrange the information data of each test point, build an information database m=[m1,m2,...,mj,...,mb], mj
=[(xj,yj),cj,dj], wherein j and b is natural number, and 1≤j≤b, b are test point sum, mjRepresent j-th test point pair
The message data set answered；
Step 6, after ultrasonic probe device finishes to tested steel plate scanning, for arbitrary coordinate be (xn,yn) detection
Point, wherein 1≤n≤b, n and b are positive integer, and b is test point sum；
According to its corresponding depth of defect array c[n][1]In the corresponding depth of defect value of each wave crest point, to test point (xn,
yn) corresponding message data set mnIntegrated；
I.e. test point (xn,yn) corresponding defective waveform curve bnIn, in two adjacent successively wave crest points, when after one
The absolute value that wave crest point corresponding depth of defect value deducts the difference of previous wave crest point corresponding depth of defect value is less than depth phase
During closing property threshold value q, corresponding with previous wave crest point for corresponding for rear wave crest point information data information data is classified as one
Sub-information data set；Otherwise, a newly-built sub- message data set；
By that analogy, thus forming test point (xn,yn) corresponding sub-information data cluster:
pn=[pn1,pn2,...,pnm,...,pnk], wherein m and k is positive integer, and 1≤m≤k, k are test point (xn,yn)
Corresponding sub-information data set sum, pnmRepresent test point (xn,yn) corresponding sub-information data cluster pnIn m-th son letter
Breath data set；
For sub-information data cluster pn=[pn1,pn2,...,pnm,...,pnk] each of sub-information data set, obtain
Take its depth of defect extreme value data set ln=[ln1,ln2,...,lnm,...,lnk], lnm=(maxc[nm][1],minc[nm][1]), its
Middle m and k is positive integer, and 1≤m≤k, k are test point (xn,yn) corresponding sub-information data set sum, lnmRepresent test point
(xn,yn) corresponding sub-information data cluster pnIn m-th sub-information data set pnmIn depth of defect extreme value, maxc[nm][1]
Represent its depth of defect maximum, minc[nm][1]Represent its depth of defect minima；
Form new information database m'=[p after corresponding for all test points wave crest point information data is integrated1,
p2,...,pn,...,pb], wherein n and b is natural number, and 1≤n≤b, b are test point sum, pnRepresent n-th test point
(xn,yn) corresponding sub-information data cluster；
Obtain the defect that in all test points corresponding sub-information data cluster, each sub-information data set is corresponding deep simultaneously
Degree extreme value data set l=[l1,l2,...,ln,...,lb], wherein n and b is natural number, and 1≤n≤b, b are test point sum,
lnRepresent n-th test point (xn,yn) corresponding depth of defect extreme value data set；
It is (x for arbitrary coordinaten,yn) the corresponding depth of defect extreme value data set l of test pointn=[ln1,
ln2,...,lnm,...,lnk] each of depth of defect extreme value data, wherein m and k be positive integer, and 1≤m≤k, k are inspection
Measuring point (xn,yn) corresponding sub-information data set sum；Searching and detecting point (xn,yn) be located eight territory { (xn,yn-1)；(xn,
yn+1)；(xn-1,yn-1)；(xn-1,yn)；(xn-1,yn+1)；(xn+1,yn-1)；(xn+1,yn)；(xn+1,yn+1) in each test point relatively
Each of depth of defect extreme value data set answered depth of defect extreme value data, if depth of defect extreme value data exists handed over
Collection, then merge sub-information data set corresponding for its corresponding test point；
So, the sub-information data set of all test points is processed through search integration according to depth of defect extreme value data
Afterwards, build new defect information data base q=[q1,q2,...,qu,...,qz], wherein u and z is positive integer, and 1≤u≤z, z are
Sub- defect information data set sum, quRepresent the sub- defect information data set of u-th defect；
Step 7, calculating defect information data base q=[q1,q2,...,qu,...,qz] in each sub- defect information data set
Corresponding defect parameters, thus obtaining defect parameters array v=[v1,v2,...,vu,...,vz], wherein u and z is just whole
Number, 1≤u≤z, z are sub- defect information data set sum, vuRepresent the defect parameters of u-th sub- defect information data set；
Wherein v represents word defective data collection quIn the test point sum that comprises, x represents two phases on steel plate length direction
The distance between adjacent detection dot center, y represents the distance between two adjacent detection dot center on steel plate width direction,
maxc[e][1]Represent defective data collection quIn the corresponding depth of defect maximum of e-th coordinate points, minc[e][1]Represent number of defects
According to collection quIn the corresponding depth of defect minima of e-th coordinate points；
Calculate information database q=[q simultaneously1,q2,...,qu,...,qz] in each sub- defect information data set corresponding
Average amplitude, thus obtaining defect average amplitude arrayWherein u and z is positive integer, 1≤
U≤z, z are sub- defect information data set sum,Represent the average amplitude of u-th sub- defect information data set；
By defect parameters array v=[v1,v2,...,vu,...,vz] in each defect parameters and rarefaction defect threshold value r1
It is compared, if vu<r1, then defect parameters v are judgeduCorresponding sub- defective data collection quCorresponding steel plate defect type is thin
Loose defect；.
Weed out information database q=[q1,q2,...,qu,...,qz] in the corresponding message data set of rarefaction defect, will
The corresponding defect parameters of remaining each message data set and average amplitude respectively with lamination defect threshold value r2And amplitude thresholds f enter
Row compares, if r2<vu< 1 andThen judge defect parameters vuCorresponding sub- defective data collection quCorresponding steel plate defect class
Type is lamination defect；
Step 8, weed out information database q=[q1,q2,...,qu,...,qz] in rarefaction defect and lamination defect phase
Corresponding sub- defect information data set, remaining each sub- defect information data set is carried out pseudo color image weight on two dimensional surface
Structure, forms corresponding subset image, then carries out morphological analysis for subset image, thus obtaining steel plate defect type is to split
Stricture of vagina defect or gas hole defect；
Calculate nearly circularity t of each subset image gas hole defect to judge steel plate, calculate flexibility g of each subset image with
Judge the crack defect of steel plate；
Wherein t=s/ (π × d2/ 4), wherein t represents nearly circularity, and s represents the area that subset image comprises, and d represents subset
Farthest 2 points of distance in image, as nearly circularity t≤t then it is assumed that corresponding to the corresponding Sub Data Set of this subset image
Steel plate defect is porous defect, and wherein t is nearly roundness threshold；
G=s/ (h × l), wherein i represent flexibility, and s represents the area occupied by subset image, and h represents in subset image
Farthest 2 points of distance, l represents the mean breadth on the perpendicular direction with h direction in subset image, as flexibility g≤g, then
Judge that the steel plate defect corresponding to the corresponding Sub Data Set of this subset image is crack-type defect, wherein, g is flexibility threshold
Value.
Preferably, in described step 2, ultrasonic probe device drives arch on steel plate to move by walking aids tool, institute
State and encoder is provided with walking aids tool with the real-time coordinate data obtaining ultrasonic probe device test point on steel plate.
Easily, described ultrasonic probe device includes multiple ultrasonic probes being set up in parallel, and forms ultrasonic probe group, described
In ultrasonic probe group, the orientation of multiple ultrasonic probes is perpendicular to the scanning direction of steel plate with described ultrasonic probe device；
Correspondingly, described Ultrasound Instrument is channel ultrasonic instrument；
Walking aids tool is the inspection car with two BOGEY WHEELs, and two BOGEY WHEELs are respectively mounted an encoder；
Ultrasonic probe group is taken to after opposite side by inspection car from the side of steel plate, with one of BOGEY WHEEL as fixing point,
Another one BOGEY WHEEL is rotated, thus completing arch track route on steel plate for the ultrasonic probe device.
In order to reduce the friction between ultrasonic probe and steel plate it is ensured that ultrasound wave smoothly enters steel plate, described ultrasonic probe dress
Put during moving on tested steel plate, sprayed with tested steel plate contact area in ultrasonic probe device using spray coupling mechanism
Couplant.
Preferably, described host computer has human-computer interaction interface, inputs control command and system by human-computer interaction interface
Parameter.
Compared with prior art, it is an advantage of the current invention that: this steel plate defect recognition methods is according only to ultrasonic probe device
The echo-signal obtaining can be automatically performed the identification of steel plate defect, and calculating speed is fast, identifies quick and precisely, and need not be substantial amounts of
Sample data is trained, and reduces data amount of calculation, accelerates recognition speed, thus reducing defect recognition cost.
Brief description
Fig. 1 is the scanning layout of ultrasonic probe group in the embodiment of the present invention.
Fig. 2 is the flow chart of embodiment of the present invention light plate defect identification method.
Fig. 3 is the depth-amplitude wavy curve schematic diagram of the corresponding echo-signal of test point.
Specific embodiment
Below in conjunction with accompanying drawing embodiment, the present invention is described in further detail.
The device used in steel plate defect recognition methodss in the present embodiment includes what host computer was connected with upper machine communication
Walking aids tool that the ultrasonic probe device 1 that the communication of Ultrasound Instrument and Ultrasound Instrument is connected is connected with upper machine communication, it is arranged at
The spray coupling mechanism that the encoder being connected on walking aids tool and with upper machine communication is connected with upper machine communication.
Wherein, host computer has human-computer interaction interface, can be inputted to ultrasonic probe device 1 by human-computer interaction interface
Control command and systematic parameter.
Ultrasonic probe device 1 in the present embodiment be set up in parallel by eight, frequency be 5mhz, wafer diameter be 40mm
Ultrasonic normal probe constitute ultrasonic probe group, the spacing between two neighboring ultrasonic probe be 50mm, then in two ultrasonic probes
The spacing of the heart is 90mm, the orientation of ultrasonic probe and the scanning direction to steel plate for the ultrasonic probe device 1 in ultrasonic probe group
Perpendicular.
Corresponding with ultrasonic probe group, Ultrasound Instrument be channel ultrasonic instrument, thus complete with ultrasonic probe group in each surpass
The data transmit-receive of sonic probe group.
As shown in figure 1, ultrasonic probe group drives arch on steel plate to move by walking aids tool.
In the present embodiment, walking aids tool is the inspection car with two BOGEY WHEELs, and two BOGEY WHEELs are respectively mounted one
Encoder with the real-time coordinate data obtaining each ultrasonic probe test position on steel plate, according to the spacing between each ultrasonic probe,
8 groups of coordinate datas can be obtained simultaneously.Ultrasonic probe group is taken to after opposite side by inspection car from the side of steel plate, with one of
BOGEY WHEEL is fixing point, and another one BOGEY WHEEL is rotated, thus completing arch walking on steel plate for the ultrasonic probe device 1
Route.
The present embodiment with to one piece of thickness as 100mm, the detection process of width is as 4000mm, length is as 6000mm steel plate
As a example, the steel plate defect recognition methodss in the present invention are illustrated.Setting inspection car advance during sampling interval be
Every 40mm gathers three ultrasonic signals.Then inspection car drives ultrasonic probe group to carry out arch all standing detection on steel plate, its
On light plate length direction, every group includes 6000 ÷ (50+40)=66 test point, and on steel plate width direction, every group includes 4000
40 × 3=300 test point of ÷, then test point sum on steel plate for the ultrasonic probe group is 66 × 300=19800.
As shown in Fig. 2 the steel plate defect recognition methodss in the present embodiment, comprise the following steps:
Step one, Ultrasound Instrument start and initialize, and send gain, sound in steel plate by human-computer interaction interface in Ultrasound Instrument
Speed, the triggering control parameter such as frequency and a ripple display parameters, wherein triggering frequency is set to 5mhz, and in steel plate, the velocity of sound is set to 5800m/
s.So so that the curvilinear abscissa showing on Ultrasound Instrument panel is consistent with actual grade value, adjust the scanning of a ripple aobvious
Show delay so that a wave profile display starting point is before surface of steel plate echo, setting a ripple scanning indication range is so that a wave profile
The scope of display depth distance > 100mm, thus cover steel plate thickness scope, setting a ripple scan display mode is positive wave.
Step 2, by human-computer interaction interface send in Ultrasound Instrument startup order, then ultrasonic probe device 1, spray coupling
Close mechanism and encoder and start work simultaneously, wherein ultrasonic probe device 1 according to the control parameter in Ultrasound Instrument to tested steel plate
Middle transmitting ultrasound wave.
Inspection car drives ultrasonic probe device 1 to move on tested steel plate, and spray coupling mechanism is sprayed water, by water simultaneously
Preferably it is coupled with steel plate with ensureing ultrasonic probe as the couplant between ultrasonic probe and steel plate.
Ultrasonic probe device 1 launches ultrasound wave to tested steel plate, receives the ultrasonic echo signal from steel plate simultaneously, its
In, ultrasonic echo signal include from surface of steel plate reflection initial signal, from defective locations reflection flaw indication, from steel plate
The bottom ripple signal of bottom reflection.
Utilize encoder to obtain the real-time location coordinates of ultrasonic probe device 1 each ultrasound detection point on tested steel plate simultaneously
Data, and the coordinate data of each test point is uploaded in host computer, form coordinate data group the w=[(x of test point1,y1),
(x2,y2),...,(xj,yj),...,(xb,yb)], wherein j and b is positive integer, and 1≤j≤b, b are test point sum, (xj,
yj) be j-th test point position coordinateses, xjRepresent j-th test point value on steel plate length direction, yjRepresent j-th inspection
Value on steel plate width direction for the measuring point.
If extension position concrete on steel plate is 1800mm, when width position is 800mm, its corresponding abscissa is 1800
÷ (50+40)=20, corresponding vertical coordinate is 800 ÷ 40 × 3=60, and that is, the corresponding coordinate of this position is (20,60).
Step 3, Ultrasound Instrument gather and store the echo-signal of each test point that ultrasonic probe device 1 returns.
Step 4, Ultrasound Instrument are amplified to echo-signal, filter, analog/digital conversion is processed, and according to the velocity of sound in steel plate and
Gain is processed to echo-signal, and then is directed to each test point position, echo letter in the range of the tested steel plate of corresponding acquisition
Number formed depth-amplitude wavy curve, thus forming depth-amplitude wavy curve data set a=[a1,a2,...,aj,...,
ab], wherein j and b is positive integer, and 1≤j≤b, b are test point sum, ajRepresent the depth-amplitude waveform of j-th test point
Curve.
Ultrasound Instrument uploads to the curve data in depth-amplitude wavy curve data set a in host computer.
As shown in figure 3, for initial signal, ultrasound wave returns to ultrasonic spy in surface of steel plate after a vertical reflection
Time t used by head1=10mm × 2 ÷ 5800m/s=3.45 μ s.
For bottom ripple signal, impinge perpendicularly on the ultrasound wave within steel plate and do not run into defect, directly reach steel plate bottom surface, then
It is reflected back the time t used by ultrasonic probe3=(10mm+100mm) × 2 ÷ 5800m/s=37.93 μ s.
For flaw indication: all defect echo time is distributed between 3.45us and 37.93us, Ultrasound Instrument is passed through ultrasonic
Ripple obtains corresponding depth value accordingly between 10mm and 110mm in the velocity of sound conversion in steel plate.
Step 5, host computer carry out subsequent treatment to depth-amplitude wavy curve data set a.
First, obtain each test point corresponding initial signal amplitude from depth-amplitude wavy curve data set a, from
And form initial signal amplitude data group i=[i1,i2,...,ij,...,ib], from depth-amplitude wavy curve data set a
Obtain each test point corresponding bottom wave amplitude, thus forming bottom wave amplitude data set d=[d1,d2,...,dj,...,db]；
Calculate initial signal amplitude meansigma methodss
Calculate bottom wave amplitude meansigma methodss
Wherein j and b is natural number, and 1≤j≤b, b are test point sum, djRepresent that j-th test point is corresponding at it
Depth-amplitude wavy curve ajUpper corresponding bottom ripple signal amplitude.
Pretreatment is carried out to depth-amplitude wavy curve data set a, that is, to every in depth-amplitude wavy curve data set a
Corresponding depth-amplitude the wavy curve of individual test point is intercepted, and retains in surface to the bottom surface depth bounds of tested steel plate and returns
The corresponding waveform of ripple signal, that is, retain waveform letter between 10mm and 110mm for the depth value in its depth-amplitude wavy curve
Breath, thus form defective waveform curve data group b=[b1,b2,...,bj,...,bb], wherein j and b is positive integer, 1≤j
≤ b, bjRepresent the defective waveform curve of j-th test point, b is test point sum.
Derived function is carried out to each test point corresponding defective waveform curve in defective waveform curve data group b, thus
Obtain all wave crest points in corresponding test point defective waveform curve, thus building wave crest point information data group c=[c1,
c2,...,cj,...,cb]；cj=[c[j][1],c[j][2]], c[j][1]=(sj0,sj1,...,sji,...,sja), c[j][2]=(fj0,
fj1,...,fji,...,fja).
Wherein j and b is natural number, and 1≤j≤b, b are test point sum, cjRepresent the defective waveform of j-th test point
Curve bjIn all wave crest point information data groups of comprising；I and a is natural number, and 0≤i≤a, a are wave crest point sum, c[j][1]
Represent the depth of defect array of j-th test point, sjiDefective waveform curve b for j-th test pointjIn i-th wave crest point pair
The depth of defect value answered, c[j][2]For the defect amplitudes array of j-th test point, fjiDefective waveform curve for j-th test point
bjIn the corresponding flaw indication amplitude of i-th wave crest point.
Arrange the information data of each test point, build an information database m=[m1,m2,...,mj,...,mb], mj
=[(xj,yj),cj,dj], wherein j and b is natural number, and 1≤j≤b, b are test point sum, mjRepresent j-th test point pair
The message data set answered.
Step 6, after ultrasonic probe device 1 finishes to tested steel plate scanning, for arbitrary coordinate be (xn,yn) detection
Point, wherein 1≤n≤b, n and b are positive integer, and b is test point sum.According to its corresponding depth of defect array c[n][1]In each
Wave crest point corresponding depth of defect value, to test point (xn,yn) corresponding message data set mnIntegrated.
I.e. test point (xn,yn) corresponding defective waveform curve bnIn, in two adjacent successively wave crest points, when after one
The absolute value that wave crest point corresponding depth of defect value deducts the difference of previous wave crest point corresponding depth of defect value is less than depth phase
During closing property threshold value q, corresponding with previous wave crest point for corresponding for rear wave crest point information data information data is classified as one
Sub-information data set.Otherwise, a newly-built sub- message data set.According to the statistical data in this field, the depth in the present embodiment
Relevance threshold q=3mm.
By that analogy, thus forming test point (xn,yn) corresponding sub-information data cluster:
pn=[pn1,pn2,...,pnm,...,pnk], wherein m and k is positive integer, and 1≤m≤k, k are test point (xn,yn)
Corresponding sub-information data set sum, pnmRepresent test point (xn,yn) corresponding sub-information data cluster pnIn m-th son letter
Breath data set.
For sub-information data cluster pn=[pn1,pn2,...,pnm,...,pnk] each of sub-information data set, obtain
Take its depth of defect extreme value data set ln=[ln1,ln2,...,lnm,...,lnk], lnm=(maxc[nm][1],minc[nm][1]), its
Middle m and k is positive integer, and 1≤m≤k, k are test point (xn,yn) corresponding sub-information data set sum, lnmRepresent test point
(xn,yn) corresponding sub-information data cluster pnIn m-th sub-information data set pnmIn depth of defect extreme value, maxc[nm][1]
Represent its depth of defect maximum, minc[nm][1]Represent its depth of defect minima.
Form new information database m'=[p after corresponding for all test points wave crest point information data is integrated1,
p2,...,pn,...,pb], wherein n and b is natural number, and 1≤n≤b, b are test point sum, pnRepresent n-th test point
(xn,yn) corresponding sub-information data cluster.
Obtain the defect that in all test points corresponding sub-information data cluster, each sub-information data set is corresponding deep simultaneously
Degree extreme value data set l=[l1,l2,...,ln,...,lb], wherein n and b is natural number, and 1≤n≤b, b are test point sum,
lnRepresent n-th test point (xn,yn) corresponding depth of defect extreme value data set.
Test point (xn,yn) the corresponding concrete calculating process of sub-information data cluster is: test point (xn,yn) corresponding
Defective waveform curve bnIn the corresponding information data of first wave peak dot be { (xn,yn)；[sn1,fn1]；dn, second wave crest point
Corresponding information data is { (xn,yn)；[sn2,fn2]；dn, wherein sn1、sn1Represent defective waveform curve b respectivelynIn first
Wave crest point and the depth of defect value of secondary peak point, fn2、fn2Represent defective waveform curve b respectivelynIn first wave peak dot and
The flaw indication amplitude of two wave crest points.
By { (xn,yn)；[sn1,fn1]；dnAs initial Sub Data Set pn1In first element, if sn2-sn2<q
When, then by corresponding for second wave crest point information data { (xn,yn)；[sn2,fn2]；dnIt is included into Sub Data Set pn1In.If
sn2-sn2During >=q, then set up new Sub Data Set pn2, correspondingly by corresponding for second wave crest point information data { (xn,yn)；
[sn2,fn2]；dnIt is included into Sub Data Set pn2In.By that analogy, form coordinate points (xn,yn) corresponding sub-information data set
Group pn=[pn1,pn2,...,pnm,...,pnk].
It is (x for arbitrary coordinaten,yn) the corresponding depth of defect extreme value data set l of test pointn=[ln1,
ln2,...,lnm,...,lnk] each of depth of defect extreme value data, wherein m and k be positive integer, and 1≤m≤k, k are inspection
Measuring point (xn,yn) corresponding sub-information data set sum；Searching and detecting point (xn,yn) be located eight territory { (xn,yn-1)；(xn,
yn+1)；(xn-1,yn-1)；(xn-1,yn)；(xn-1,yn+1)；(xn+1,yn-1)；(xn+1,yn)；(xn+1,yn+1) in each test point relatively
Each of depth of defect extreme value data set answered depth of defect extreme value data, if depth of defect extreme value data exists handed over
Collection, then merge sub-information data set corresponding for its corresponding test point.
As follows to illustrate: coordinate message data set p in the test point of (20,60)xyCorresponding defect is deep
Degree extreme value lxy=(80,100).Searching and detecting point (20,60) be located eight territories (20,59), (20,61), (19,59),
(19,60), (19,61), (21,59), (21,60), (21,61) } in all sub-information data sets, if test point (20,
61) there is a sub- message data set p in corresponding sub-information data clusterabCorresponding depth of defect extreme value lab=(90,
110), lxyWith labExist and occur simultaneously (90,100), then by lxyAnd labMerge into a new message data set qx.
So, the sub-information data set of all test points is processed through search integration according to depth of defect extreme value data
Afterwards, build new defect information data base q=[q1,q2,...,qu,...,qz], wherein u and z is positive integer, and 1≤u≤z, z are
Sub- defect information data set sum, quRepresent the sub- defect information data set of u-th defect.
Step 7, calculating defect information data base q=[q1,q2,...,qu,...,qz] in each sub- defect information data set
Corresponding defect parameters, thus obtaining defect parameters array v=[v1,v2,...,vu,...,vz], wherein u and z is just whole
Number, 1≤u≤z, z are sub- defect information data set sum, vuRepresent the defect parameters of u-th sub- defect information data set；
Wherein v represents word defective data collection quIn the test point sum that comprises, x represents two phases on steel plate length direction
The distance between adjacent detection dot center, y represents the distance between two adjacent detection dot center on steel plate width direction, this
X=90mm in embodiment, y=40/3mm, maxc[e][1]Represent defective data collection quIn the corresponding depth of defect of e-th coordinate points
Maximum, minc[e][1]Represent defective data collection quIn the corresponding depth of defect minima of e-th coordinate points.
Calculate information database q=[q simultaneously1,q2,...,qu,...,qz] in each sub- defect information data set corresponding
Average amplitude, thus obtaining defect average amplitude arrayWherein u and z is positive integer, 1≤
U≤z, z are sub- defect information data set sum,Represent the average amplitude of u-th sub- defect information data set.
By defect parameters array v=[v1,v2,...,vu,...,vz] in each defect parameters and rarefaction defect threshold value r1
It is compared, if vu<r1, then defect parameters v are judgeduCorresponding sub- defective data collection quCorresponding steel plate defect type is thin
Loose defect.R in the present embodiment1=0.5, this r1It is worth for experiment statisticses value.
Weed out information database q=[q1,q2,...,qu,...,qz] in the corresponding message data set of rarefaction defect, will
The corresponding defect parameters of remaining each message data set and average amplitude respectively with lamination defect threshold value r2And amplitude thresholds f enter
Row compares, if r2<vu< 1 andThen judge defect parameters vuCorresponding sub- defective data collection quCorresponding steel plate defect class
Type is lamination defect.R in the present embodiment2=0.8,
Step 8, weed out information database q=[q1,q2,...,qu,...,qz] in rarefaction defect and lamination defect phase
Corresponding sub- defect information data set, remaining each sub- defect information data set is carried out pseudo color image weight on two dimensional surface
Structure, forms corresponding subset image, then carries out morphological analysis for subset image, thus obtaining steel plate defect type is to split
Stricture of vagina defect or gas hole defect；
Calculate nearly circularity t of each subset image gas hole defect to judge steel plate, calculate flexibility g of each subset image with
Judge the crack defect of steel plate；
Wherein t=s/ (π × d2/ 4), wherein t represents nearly circularity, and s represents the area that subset image comprises, and d represents subset
Farthest 2 points of distance in image, as nearly circularity t≤t then it is assumed that corresponding to the corresponding Sub Data Set of this subset image
Steel plate defect is porous defect；Wherein t is nearly roundness threshold, t=0.8 in the present embodiment.
G=s/ (h × l), wherein i represent flexibility, and s represents the area occupied by subset image, and h represents in subset image
Farthest 2 points of distance, l represents the mean breadth on the perpendicular direction with h direction in subset image, as flexibility g≤g, then
Judge that the steel plate defect corresponding to the corresponding Sub Data Set of this subset image is crack-type defect；Wherein, g is flexibility threshold
Value, g=0.2 in the present embodiment.
Claims (5)
1. a kind of steel plate defect recognition methodss it is characterised in that: comprise the following steps:
Step one, Ultrasound Instrument start and initialize, and host computer sends control parameter to Ultrasound Instrument；
Step 2, ultrasonic probe device (1) start and are operated according to the control parameter in Ultrasound Instrument, ultrasonic probe device
(1) move on tested steel plate, host computer obtains the real-time location coordinates data to steel plate test point for the ultrasonic probe device (1),
Form coordinate data group the w=[(x of test point1,y1),(x2,y2),...,(xj,yj),...,(xb,yb)], wherein j and b is
Positive integer, 1≤j≤b, b are test point sum, (xj,yj) be j-th test point position coordinateses, xjRepresent j-th test point
Value on steel plate length direction, yjRepresent j-th test point value on steel plate width direction；
Ultrasonic probe device (1) launches ultrasound wave to tested steel plate, receives the ultrasonic echo signal from steel plate simultaneously, its
In, ultrasonic echo signal include from surface of steel plate reflection initial signal, from defective locations reflection flaw indication, from steel plate
The bottom ripple signal of bottom reflection；
Step 3, Ultrasound Instrument gather and store the echo-signal of each test point that ultrasonic probe device (1) returns；
Step 4, Ultrasound Instrument are processed to echo-signal according to the velocity of sound in steel plate and gain, and then are directed to each test point position
Put, the corresponding depth-amplitude wavy curve obtaining echo-signal formation in the range of tested steel plate, thus form depth-amplitude
Wavy curve data set a=[a1,a2,...,aj,...,ab], wherein j and b is positive integer, and 1≤j≤b, b are that test point is total
Number, ajRepresent the depth-amplitude wavy curve of j-th test point；
Ultrasound Instrument uploads to the curve data in depth-amplitude wavy curve data set a in host computer；
Step 5, host computer carry out subsequent treatment to depth-amplitude wavy curve data set a；
First, obtain each test point corresponding initial signal amplitude from depth-amplitude wavy curve data set a, thus shape
Become initial signal amplitude data group i=[i1,i2,...,ij,...,ib], obtain from depth-amplitude wavy curve data set a
Each test point corresponding bottom wave amplitude, thus form bottom wave amplitude data set d=[d1,d2,...,dj,...,db]；
Calculate initial signal amplitude meansigma methodss
Calculate bottom wave amplitude meansigma methodss
Wherein j and b is natural number, and 1≤j≤b, b are test point sum, djRepresent j-th test point its corresponding depth-
Amplitude wavy curve ajUpper corresponding bottom ripple signal amplitude；
Pretreatment is carried out to depth-amplitude wavy curve data set a, that is, to each inspection in depth-amplitude wavy curve data set a
The corresponding depth of measuring point-amplitude wavy curve is intercepted, and retains echo letter in surface to the bottom surface depth bounds of tested steel plate
Number corresponding waveform, thus form defective waveform curve data group b=[b1,b2,...,bj,...,bb], wherein j and b is just
Integer, 1≤j≤b, bjRepresent the defective waveform curve of j-th test point, b is test point sum；
Derived function is carried out to each test point corresponding defective waveform curve in defective waveform curve data group b, thus obtaining
All wave crest points in corresponding test point defective waveform curve, thus build wave crest point information data group c=[c1,c2,...,
cj,...,cb]；cj=[c[j][1],c[j][2]], c[j][1]=(sj0,sj1,...,sji,...,sja), c[j][2]=(fj0,fj1,...,
fji,...,fja)；
Wherein j and b is natural number, and 1≤j≤b, b are test point sum, cjRepresent the defective waveform curve b of j-th test pointj
In all wave crest point information data groups of comprising；I and a is natural number, and 0≤i≤a, a are wave crest point sum, c[j][1]Represent the
The depth of defect array of j test point, sjiDefective waveform curve b for j-th test pointjIn i-th wave crest point is corresponding lacks
Sunken depth value, c[j][2]For the defect amplitudes array of j-th test point, fjiDefective waveform curve b for j-th test pointjIn
The corresponding flaw indication amplitude of i wave crest point；
Arrange the information data of each test point, build an information database m=[m1,m2,...,mj,...,mb], mj=
[(xj,yj),cj,dj], wherein j and b is natural number, and 1≤j≤b, b are test point sum, mjRepresent that j-th test point corresponds to
Message data set；
Step 6, after ultrasonic probe device (1) finishes to tested steel plate scanning, for arbitrary coordinate be (xn,yn) detection
Point, wherein 1≤n≤b, n and b are positive integer, and b is test point sum；
According to its corresponding depth of defect array c[n][1]In the corresponding depth of defect value of each wave crest point, to test point (xn,yn) phase
Corresponding message data set mnIntegrated；
I.e. test point (xn,yn) corresponding defective waveform curve bnIn, in two adjacent successively wave crest points, when after one crest
The absolute value that the corresponding depth of defect value of point deducts the difference of previous wave crest point corresponding depth of defect value is less than depth correlation
During threshold value q, corresponding with previous wave crest point for corresponding for rear wave crest point information data information data is classified as a son letter
Breath data set；Otherwise, a newly-built sub- message data set；
By that analogy, thus forming test point (xn,yn) corresponding sub-information data cluster:
pn=[pn1,pn2,...,pnm,...,pnk], wherein m and k is positive integer, and 1≤m≤k, k are test point (xn,yn) corresponding
Sub-information data set sum, pnmRepresent test point (xn,yn) corresponding sub-information data cluster pnIn m-th sub-information number
According to collection；
For sub-information data cluster pn=[pn1,pn2,...,pnm,...,pnk] each of sub-information data set, obtain it
Depth of defect extreme value data set ln=[ln1,ln2,...,lnm,...,lnk], lnm=(maxc[nm][1],minc[nm][1]), wherein m
It is positive integer with k, 1≤m≤k, k are test point (xn,yn) corresponding sub-information data set sum, lnmRepresent test point (xn,
yn) corresponding sub-information data cluster pnIn m-th sub-information data set pnmIn depth of defect extreme value, maxc[nm][1]Represent
Its depth of defect maximum, minc[nm][1]Represent its depth of defect minima；
Form new information database m'=[p after corresponding for all test points wave crest point information data is integrated1,
p2,...,pn,...,pb], wherein n and b is natural number, and 1≤n≤b, b are test point sum, pnRepresent n-th test point
(xn,yn) corresponding sub-information data cluster；
Obtain the corresponding depth of defect pole of each sub-information data set in all test points corresponding sub-information data cluster simultaneously
Value Data collection l=[l1,l2,...,ln,...,lb], wherein n and b is natural number, and 1≤n≤b, b are test point sum, lnTable
Show n-th test point (xn,yn) corresponding depth of defect extreme value data set；
It is (x for arbitrary coordinaten,yn) the corresponding depth of defect extreme value data set l of test pointn=[ln1,ln2,...,
lnm,...,lnk] each of depth of defect extreme value data, wherein m and k be positive integer, and 1≤m≤k, k are test point (xn,
yn) corresponding sub-information data set sum；Searching and detecting point (xn,yn) be located eight territory { (xn,yn-1)；(xn,yn+1)；
(xn-1,yn-1)；(xn-1,yn)；(xn-1,yn+1)；(xn+1,yn-1)；(xn+1,yn)；(xn+1,yn+1) in each test point corresponding lack
Each of sunken depth extreme value data set depth of defect extreme value data, if depth of defect extreme value data exists occured simultaneously, will
The corresponding sub-information data set of its corresponding test point merges；
So, by the sub-information data set of all test points according to depth of defect extreme value data through search integration process after, structure
Build new defect information data base q=[q1,q2,...,qu,...,qz], wherein u and z is positive integer, and 1≤u≤z, z are that son lacks
Sunken message data set sum, quRepresent the sub- defect information data set of u-th defect；
Step 7, calculating defect information data base q=[q1,q2,...,qu,...,qz] in each sub- defect information data set relative
The defect parameters answered, thus obtain defect parameters array v=[v1,v2,...,vu,...,vz], wherein u and z is positive integer, 1≤
U≤z, z are sub- defect information data set sum, vuRepresent the defect parameters of u-th sub- defect information data set；
Wherein v represents word defective data collection quIn the test point sum that comprises, x represents that on steel plate length direction two are adjacent
The distance between detection dot center, y represents the distance between two adjacent detection dot center on steel plate width direction,
maxc[e][1]Represent defective data collection quIn the corresponding depth of defect maximum of e-th coordinate points, minc[e][1]Represent number of defects
According to collection quIn the corresponding depth of defect minima of e-th coordinate points；
Calculate information database q=[q simultaneously1,q2,...,qu,...,qz] in each sub- defect information data set corresponding flat
All amplitudes, thus obtain defect average amplitude arrayWherein u and z is positive integer, 1≤u≤
Z, z are sub- defect information data set sum,Represent the average amplitude of u-th sub- defect information data set；
By defect parameters array v=[v1,v2,...,vu,...,vz] in each defect parameters and rarefaction defect threshold value r1Carry out
Relatively, if vu<r1, then defect parameters v are judgeduCorresponding sub- defective data collection quCorresponding steel plate defect type is loose lacking
Fall into；.
Weed out information database q=[q1,q2,...,qu,...,qz] in the corresponding message data set of rarefaction defect, will be remaining
The corresponding defect parameters of each message data set and average amplitude respectively with lamination defect threshold value r2And amplitude thresholds f are compared
Relatively, if r2<vu< 1 andThen judge defect parameters vuCorresponding sub- defective data collection quCorresponding steel plate defect type is
Lamination defect；
Step 8, weed out information database q=[q1,q2,...,qu,...,qz] in rarefaction defect corresponding with lamination defect
Sub- defect information data set, remaining each sub- defect information data set is carried out pseudo color image reconstruct, shape on two dimensional surface
Become corresponding subset image, then carry out morphological analysis for subset image, thus obtain steel plate defect type lacking for crackle
Fall into or gas hole defect；
Calculate nearly circularity t of each subset image gas hole defect to judge steel plate, flexibility g calculating each subset image is to judge
The crack defect of steel plate；
Wherein t=s/ (π × d2/ 4), wherein t represents nearly circularity, and s represents the area that subset image comprises, and d represents in subset image
Farthest 2 points of distance, the steel plate as nearly circularity t≤t then it is assumed that corresponding to the corresponding Sub Data Set of this subset image lacks
Fall into as porous defect, wherein t is nearly roundness threshold；
G=s/ (h × l), wherein i represent flexibility, and s represents the area occupied by subset image, and h represents farthest in subset image
2 points of distance, l represents the mean breadth on the perpendicular direction with h direction in subset image, as flexibility g≤g, then judges
Steel plate defect corresponding to the corresponding Sub Data Set of this subset image is crack-type defect, and wherein, g is flexibility threshold value.
2. steel plate defect recognition methodss according to claim 1 it is characterised in that: in described step 2, ultrasonic probe fill
Putting (1) drives arch on steel plate to move by walking aids tool, and described walking aids tool is provided with encoder with real-time
Obtain the coordinate data of ultrasonic probe device (1) test point on steel plate.
3. steel plate defect recognition methodss according to claim 2 it is characterised in that: described ultrasonic probe device (1) includes
Multiple ultrasonic probes being set up in parallel, form ultrasonic probe group, the orientation of multiple ultrasonic probes in described ultrasonic probe group
Perpendicular to the scanning direction of steel plate with described ultrasonic probe device (1)；
Correspondingly, described Ultrasound Instrument is channel ultrasonic instrument；
Walking aids tool is the inspection car with two BOGEY WHEELs, and two BOGEY WHEELs are respectively mounted an encoder；
Ultrasonic probe group is taken to after opposite side by inspection car from the side of steel plate, with one of BOGEY WHEEL as fixing point, in addition
One BOGEY WHEEL is rotated, thus completing arch track route on steel plate for the ultrasonic probe device (1).
4. steel plate defect recognition methodss according to claim 2 it is characterised in that: described ultrasonic probe device (1) is in quilt
Survey during moving on steel plate, spray coupling in ultrasonic probe device (1) with tested steel plate contact area using spray coupling mechanism
Mixture.
5. steel plate defect recognition methodss according to claim 1 it is characterised in that: described host computer has man-machine interaction circle
Face, inputs control command and systematic parameter by human-computer interaction interface.
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| KR101736612B1 (en) * | 2015-12-07 | 2017-05-17 | 주식회사 포스코 | Apparatus and method of detecting inner defect of steel plate using height controllable ultrasonic sensor |
| CN108956761B (en) * | 2017-05-23 | 2020-12-25 | 浙江工商职业技术学院 | Steel plate full-coverage ultrasonic detection device and method |
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| CN109613124A (en) * | 2018-12-05 | 2019-04-12 | 鞍钢股份有限公司 | A kind of steel plate automatic ultrasonic testing device and detection method |
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| CN110108664A (en) * | 2019-04-16 | 2019-08-09 | 国网江苏省电力有限公司电力科学研究院 | A kind of lossless detection method of composite material cross arm defect |
| CN112288678B (en) * | 2019-09-11 | 2022-05-27 | 中北大学 | Pseudo-color display method and system for welding seam image |
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| CN113671039A (en) * | 2021-07-31 | 2021-11-19 | 南京中车浦镇城轨车辆有限责任公司 | Polyurethane adhesive defect measuring method and system based on ultrasonic detection |
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